智能电网
智能電網
지능전망
Smart Grid
2015年
8期
711-716
,共6页
检修计划优化%电网风险%负荷转移%改进的粒子群算法
檢脩計劃優化%電網風險%負荷轉移%改進的粒子群算法
검수계화우화%전망풍험%부하전이%개진적입자군산법
optimization of maintenance schedule%risk of power grid%load transfer%improved particle swarm algorithm
当电网处于检修状态时,负荷转移、线路负载较重等情况使电网处于更加脆弱的状态,科学地估量电网检修时段的风险大小对电网的可靠、经济运行意义重大。鉴于负荷转移路径选择对电网和用户风险水平均有影响,在考虑电网运行风险的同时加入负荷转移方面的因素,通过将风险进行经济性量化的方法,建立以检修引起的综合风险费用最小为目标函数,并考虑相关约束条件的优化模型,进一步降低检修时期电网的运行风险。考虑该模型存在多方面约束条件的特点,利用改进的粒子群算法求取最优解。通过Matlab软件编程,以6母线RBTS系统为案例进行求解,计算结果表明,模型能有效地通过优化检修计划降低电网检修时期的风险值。
噹電網處于檢脩狀態時,負荷轉移、線路負載較重等情況使電網處于更加脆弱的狀態,科學地估量電網檢脩時段的風險大小對電網的可靠、經濟運行意義重大。鑒于負荷轉移路徑選擇對電網和用戶風險水平均有影響,在攷慮電網運行風險的同時加入負荷轉移方麵的因素,通過將風險進行經濟性量化的方法,建立以檢脩引起的綜閤風險費用最小為目標函數,併攷慮相關約束條件的優化模型,進一步降低檢脩時期電網的運行風險。攷慮該模型存在多方麵約束條件的特點,利用改進的粒子群算法求取最優解。通過Matlab軟件編程,以6母線RBTS繫統為案例進行求解,計算結果錶明,模型能有效地通過優化檢脩計劃降低電網檢脩時期的風險值。
당전망처우검수상태시,부하전이、선로부재교중등정황사전망처우경가취약적상태,과학지고량전망검수시단적풍험대소대전망적가고、경제운행의의중대。감우부하전이로경선택대전망화용호풍험수평균유영향,재고필전망운행풍험적동시가입부하전이방면적인소,통과장풍험진행경제성양화적방법,건립이검수인기적종합풍험비용최소위목표함수,병고필상관약속조건적우화모형,진일보강저검수시기전망적운행풍험。고필해모형존재다방면약속조건적특점,이용개진적입자군산법구취최우해。통과Matlab연건편정,이6모선RBTS계통위안례진행구해,계산결과표명,모형능유효지통과우화검수계화강저전망검수시기적풍험치。
Considering of the load transfer and heavier load of transmission lines, power grid is more fragile during the maintenance time compared with normal state, therefore, estimating the risk of power grid during the maintenance time scientifically is of great significance to reliability and economy of power grid. As different load transmission paths influence the risk level of power grid and users, how to choose more scientific load transmission path is considered as well as the risk of power grid. For further reducing the risk level of power system operation during maintenance time, the model’s objective function is the minimum of the comprehensive risk cost caused by maintenance, the optimization model of the related constraint conditions are taking into account. As many constraint conditions exist within the power grid, this paper prefers the improved particle swarm algorithm to achieve a better optimal solution. By means of software programming, the analysis of the RBTS system example shows the model here can effectively reduce the risk level of power grid during maintenance time via getting optimized maintenance plan.